Evaluating limited influence estimators of core inflation in New Zealand

T he core inflation measures—which exclude food and energy—are commonly used in monetary policy deliberations. Recently, the Federal Open Market Committee (FOMC) has been accused of being out of touch with consumers because the prices of groceries and gas have increased much more rapidly than the prices of the goods reflected in the core measures.1 In a recent speech, Janet Yellen, Vice Chair of the Fed’s Board of Governors, defended the FOMC’s use of core measures, saying that the Committee’s “focus on core and other inflation measures that may exclude recent increases in the cost of gasoline and other household essentials is not intended to downplay the importance of these items in the cost of living or to lower the bar on the definition of price stability.” Rather, she said, “The Federal Reserve aims to stabilize inflation across the entire basket of goods and services that households purchase, including energy and food,” but the FOMC focuses on core inflation measures because “in light of the volatility of food and energy prices, core inflation has been a better forecaster of overall inflation in the medium term than overall inflation itself has been over the past 25 years.”2 This essay notes that the evidence that core inflation is a better predictor of future headline inflation is mixed and presents the results of a simple test of the proposition that core inflation is a better predictor of future headline inflation than headline inflation. The essay concludes by showing that over periods of interest to consumers, the difference in the loss of purchasing power reflected by the core and headline measures is economically relevant. Not only is the evidence of the relative predictive power of the core versus the headline measures mixed, but much of the pertinent literature investigates the predictive power of core inflation measures that differ from those used by the FOMC.3 Consequently, surprisingly few studies address the hypothesis that core inflation used by the FOMC better predicts future headline inflation than headline inflation itself. In addition, much of the existing research uses a one-year forecast horizon, which is likely shorter than the “medium-term” horizon of interest to policymakers. Speci fically, I test whether the average level of core inflation or headline inflation over the most recent two or three years is a better predictor of headline inflation over the next two or three years.4 Twoand three-year forecast horizons are assumed to represent the “medium term.” I use a standard procedure to test whether the difference in the two forecasts is statistically significant in terms of either the mean square forecast error (MSFE) or the mean absolute forecast error (MAFE). Vice Chair Yellen did not specify a particular inflation index, so the analysis presented Core Versus Headline Inflation: An Opportunity for Greater Transparency

This paper measures core inflation and evaluates their performance in Nepal. Concept of core inflation and survey of methodologies as well as international experiences and empirical evidence have been reviewed and analysed before measuring the core inflation in Nepal. An existence of asymmetric price distribution necessitates the alternative measure of inflation in place of traditional CPI-based headline inflation. Ultimately, by using exclusion method and stochastic measures, nine different types of core inflations have been computed. Empirical results show that core inflations perform better than the headline inflation in several aspects such as having less volatility, highly relating to money supply growth, and capturing the permanent component of headline inflation. Better core inflation is selected with the help of different performance criteria such as observing statistical properties, tracking trend inflation, finding the relationship with money supply, and the analysing the ability of capturing permanent component and forecasting capacity. However, no single core measure performs better in all aspects. Hence, a set of core inflation measures, namely exclusion based, weighted median and trimmed mean should be analysed in conducting the monetary policy in Nepal.

In view of formulating credible monetary policy to attain the price stability objective, the difficult part for the central banks is to distinguish, within ongoing inflation evolutions, between short term volatility and the underlying pressure of inflation. While it has now become a standard practice for most central banks around the world to monitor core inflation, little progress has so far been made in the Indian context. This paper takes a pioneering look in measuring core inflation in India focusing on the popular exclusion and trimmed mean approaches. The performance criteria adopted in this analysis show that the measure of core inflation developed in the paper has strong money-induced characteristics and therefore, can credibly be used as a short or medium term guide of monetary policy in India. This paper aims to introduce the concept of core inflation and to calculate alternative measures of core inflation for India. We have used two approaches: (1) Exclusion based approach (2) Trimmed mean method, to identify the measures of core inflation. We have obtained five alternative measures of core inflation using exclusion approach these are: WPI excluding food articles, WPI excluding food articles and non food articles, WPI excluding food articles and fuel (minerals oils), WPI excluding fuel (minerals oils) and manufactured products (Beverages, Tobacco and Tobacco Products, Non-Metallic Mineral Products, Basic Metals Alloys and Metals Products), WPI excluding Minerals, Basic Metals Alloys and Metals Products, Minerals Oils, Non-Metallic Mineral Products, Beverages, Tobacco and Tobacco Products., two measures TRIM15_15 and TRIM20_20 using trimmed mean For this we have evaluated these measures of core inflation in India based on new series of WPI (1993–94 =100). These measures were tested for volatility and unbiasedness and co- integration with headline inflation. The co-integration test statistics confirm long run relationship between the core inflation measures and headline inflation. The study found that the exclusion based measures are more appropriate for inflation targeting purposes and the measures based on the trimming may be better for identifying the underlying trends and also in providing a robust forecast of future inflation rates in India.

This paper undertakes a comprehensive examination of 10 measures of core inflation and evaluates which measure produces the best forecast of headline inflation out‐of‐sample. We use the Personal Consumption Expenditure Price Index as our measure of inflation. We use two sets of components (17 and 50) of the Personal Consumption Expenditure Price Index to construct these core inflation measures and evaluate these measures at the three time horizons (6, 12 and 24 months) most relevant for monetary policy decisions. The best measure of core inflation for both sets of components and over all time horizons uses weights based on the first principal component of the disaggregated (component‐level) prices. Interestingly, the results vary by the number of components used; when more components are used the weights based on the persistence of each component is statistically equivalent to the weights generated by the first principal component. However, those forecasts using the persistence of 50 components are statistically worse than those generated using the first principal component of 17 components. The statistical superiority of the principal component method is due to the fact that it extracts (in the first principal component) the common source of variation in the component level prices that accurately describes trend inflation over the next 6–24 months.

What are the implications of targeting different measures of inflation? We extend a basic theoretical framework of optimal monetary policy under inflation targeting (Svensson 1997) to include several components of CPI inflation, and analyse the implications of using different measures of inflation as the target variable – headline CPI inflation, core inflation, and CPI excluding interest rates. Our main results are the following. First, barring the interest rate component, temporary shocks to inflation do not affect optimal monetary policy under any regime. Second, indirect (second‐round) effects of disturbances on target variables need to be accounted for properly. Simply excluding seemingly temporary disturbances from the reaction function risks leading to inappropriate policy responses. Third, it may be optimal to respond to changes in one measure of inflation even if the target is defined in terms of another. Fourth, the presence of the direct interest rate component in the CPI tends to push optimal monetary policy in an expansionary direction. The net effect, considering also the traditional channel, however, depends on the nature of the initial disturbance.

Existing measures of core inflation ignore a part of ‘should be’ the core inflation. Exclusion based measures ‘exclude’ a part of persistent inflation inherently existing in the excluded part whereas filter based measures ‘filter-out’ the cyclical part also rather than the irregular component only. This study proposes a new idea to define and measure core inflation – noise free inflation or denoised inflation. As against considering only trend to define core inflation, this study proposes using cyclical component also to be part of core inflation. If core inflation is to be useful, for monetary policy making, as an indicator of underlying inflation, it has to include demand related component of inflation associated with current economic cycle. By using wavelet analysis approach to decompose seasonally adjusted price index into noise, cyclical component and trend, we estimate a denoised inflation series for Pakistan for the period July 1992 to June 2017. Since denoised inflation passes ‘statistical’ as well as ‘theoretical’ tests necessary for a series to be core inflation, we think it can be used as a new core inflation measure for Pakistan. This can also be estimated and tested for any country.

The term 'core inflation' is widely used by academics and central bankers. But despite its prevalence, there is neither a commonly accepted theoretical definition nor an agreed method of measuring it. The range of conceptual bases is potentially confusing, and can make the large number of available measures of core inflation difficult to interpret, particularly when they display different trends. Nevertheless, measures of core inflation can be helpful if they increase the signal to noise ratio in measured inflation. This paper examines a range of measures of core inflation for the United Kingdom, both conceptually and empirically, setting out their motivation and highlighting their potential limitations. No single measure performs well across the board, but a compromise conclusion on the usefulness of measures of core inflation is that each one may provide a different insight into the inflation process. There can be value in looking at a range of measures, as long as one bears in mind what information each type of indicator is best at providing. When all measures are giving the same message then, in a sense, monetary policy makers can reasonably consider that these measures are providing a reliable guide to inflationary pressures. It is when the measures start to diverge that policymakers need to take a much closer look at the reasons for those divergences.

A central element of an inflation targeting approach to monetary policy is a proper measure of inflation. The international evidence suggests the use of core inflation measures. In this paper we claim that core inflation should be measured as the underlying trend of inflation that comes from nominal shocks that have no real effect in the long term. However, most of the time core inflation is computed zero weighting observations at the tail of the inflation distribution. Quah and Vahey (1996) proposed a method of computing core inflation imposing theory constraints to a SVAR specification. In this paper we present estimation for Peruvian data and compare the predictability properties of competing measures of inflation following an idea of Diebold and Killian (1997).

Two rationales offered for policymakers' focus on core measures of inflation as a guide to underlying inflation are that core inflation omits food and energy prices, which are thought to be more volatile than other components, and that core inflation is thought to be a better predictor of total inflation over time horizons of import to policymakers. The authors' investigation finds little support for either rationale. They find that food and energy prices are not the most volatile components of inflation and that depending on which inflation measure is used, core inflation is not necessarily the best predictor of total inflation. However, they do find that combining CPI and PCE inflation measures can lead to statistically significant more accurate forecasts of each inflation measure, suggesting that each measure includes independent information that can be exploited to yield better forecasts.

This paper estimates core inflation in Norway, identified as that component of inflation that has no long-run effect on GDP. The model distinguishes explicitly between domestic and imported core inflation. The results show that (domestic) core inflation is the main component of CPI inflation. CPI inflation, however, misrepresents core inflation in some periods. The differences are well explained by the other shocks identified in the model, in particular the oil price shocks of the 1970s when Norway imported inflation, and the negative noncore (supply) shocks of the late 1980s, which pushed inflation up temporarily relative to core inflation.

This paper estimates core inflation in Norway, identified as that component of inflation that has no long-run effect on GDP. The model distinguishes explicitly between domestic and imported core inflation. The results show that (domestic) core inflation is the main component of CPI inflation. CPI inflation, however, misrepresents core inflation in some periods. The differences are well explained by the other shocks identified in the model, in particular the oil price shocks of the 1970s when Norway imported inflation, and the negative noncore (supply) shocks of the late 1980s, which pushed inflation up temporarily relative to core inflation.

This paper estimates core inflation in Norway, identified as that component of inflation that has no long-run effect on GDP. The model distinguishes explicitly between domestic and imported core inflation. The results show that (domestic) core inflation is the main component of CPI inflation. However, CPI inflation misrepresents core inflation during some periods. The differences are well explained by the other shocks identified in the model, in particular the oil price shocks of the 1970s when Norway imported inflation, and the negative non-core (supply) shocks of the late 1980s, which pushed inflation up temporarily relative to core inflation.

Does excluding food and energy prices from the Consumer Price Index (CPI) produce a measure that captures permanent price changes? To examine this question we decompose CPI inflation and inflation into their permanent and transitory components using a correlated unobserved components model. One of the key aspects of the correlated unobserved components model is that it allows shocks to the permanent component to potentially be more variable than shocks to the series itself, due to offsetting transitory shocks correlated with the permanent shocks. The stationarity of inflation may be time-varying, so we examine the performance of the core measure of inflation for periods during which it appears that inflation is I(1) and for periods during which it appears that inflation is I(0). For a period in which inflation appears to be I(1), we find that core inflation and the permanent component of overall inflation are closely related, although core inflation does have some drawbacks as a measure of permanent inflation. For a period in which inflation appears to be I(0), we decompose the core and overall price levels and find that the permanent component of core CPI is much more volatile than the actual core series and that core excludes volatile permanent shocks to the overall price level.

Does excluding food and energy prices from the Consumer Price Index (CPI) produce a measure that captures permanent price changes? To examine this question we decompose CPI inflation and “core” inflation into their permanent and transitory components using a correlated unobserved components model. One of the key aspects of the correlated unobserved components model is that it allows shocks to the permanent component to potentially be more variable than shocks to the series itself, due to offsetting transitory shocks correlated with the permanent shocks. The stationarity of inflation may be time-varying, so we examine the performance of the core measure of inflation for periods during which it appears that inflation is I(1) and for periods during which it appears that inflation is I(0). For a period in which inflation appears to be I(1), we find that core inflation and the permanent component of overall inflation are closely related, although core inflation does have some drawbacks as a measure of permanent inflation. For a period in which inflation appears to be I(0), we decompose the core and overall price levels and find that the permanent component of core CPI is much more volatile than the actual core series and that core excludes volatile permanent shocks to the overall price level.

A note on modelling core inflation for the UK using a new dynamic factor estimation method and a large disaggregated price index dataset